Alexander Teumer,1,2 Adrienne Tin,3 Rossella Sorice,4 Mathias Gorski,5,6

Nan Cher Yeo,7 Audrey Y. Chu,8,9 Man Li,3 Yong Li,10 Vladan Mijatovic,11 Yi-An Ko,12

Daniel Taliun,13 Alessandro Luciani,14 Ming-Huei Chen,15,16 Qiong Yang,16

Meredith C. Foster,17 Matthias Olden,5,18 Linda T. Hiraki,19 Bamidele O. Tayo,20

Christian Fuchsberger,13 Aida Karina Dieffenbach,21,22 Alan R. Shuldiner,23

Albert V. Smith,24,25 Allison M. Zappa,26 Antonio Lupo,27 Barbara Kollerits,28

Belen Ponte,29 Bénédicte Stengel,30,31 Bernhard K. Krämer,32 Bernhard Paulweber,33

Braxton D. Mitchell,23 Caroline Hayward,34 Catherine Helmer,35 Christa Meisinger,36

Christian Gieger,37 Christian M. Shaffer,38 Christian Müller,39,40 Claudia Langenberg,41

Daniel Ackermann,42 David Siscovick,43 DCCT/EDIC,44 Eric Boerwinkle,45

Florian Kronenberg,28 Georg B. Ehret,46 Georg Homuth,47 Gerard Waeber,48

Gerjan Navis,49 Giovanni Gambaro,50 Giovanni Malerba,11 Gudny Eiriksdottir,24

Guo Li,43 H. Erich Wichmann,51,52,53 Harald Grallert,36,54,55

Henri Wallaschofski,56 Henry Völzke,1,2 Herrmann Brenner,21 Holly Kramer,20

I. Mateo Leach,57 Igor Rudan,58 Hans L. Hillege,59 Jacques S. Beckmann,60,61

Jean Charles Lambert,62 Jian’an Luan,41 Jing Hua Zhao,41 John Chalmers,63

Josef Coresh,3,64 Joshua C. Denny,65 Katja Butterbach,21 Lenore J. Launer,66

Luigi Ferrucci,67 Lyudmyla Kedenko,33 Margot Haun,28 Marie Metzger,30,31

Mark Woodward,3,63,68 Matthew J. Hoffman,7 Matthias Nauck,2,56

Melanie Waldenberger,36 Menno Pruijm,69 Murielle Bochud,70

Myriam Rheinberger,71 Niek Verweij,57 Nicholas J. Wareham,41 Nicole Endlich,72

Nicole Soranzo,73,74 Ozren Polasek,75 Pim van der Harst,59 Peter Paul Pramstaller,13

Peter Vollenweider,48 Philipp S. Wild,76,77,78 Ron T. Gansevoort,59 Rainer Rettig,79

Reiner Biffar,80 Robert J. Carroll,65 Ronit Katz,81 Ruth J.F. Loos,41,82 Shih-Jen Hwang,9

Stefan Coassin,28 Sven Bergmann,83 Sylvia E. Rosas,84 Sylvia Stracke,85 Tamara B. Harris,66

Tanguy Corre,83 Tanja Zeller,39,40 Thomas Illig ,36,86,87 Thor Aspelund,24,25 Toshiko Tanaka,67

Uwe Lendeckel,88 Uwe Völker,2,47 Vilmundur Gudnason,24,25 Vincent Chouraki ,62

Wolfgang Koenig,89,90,91 Zoltan Kutalik ,61,83,92 Jeffrey R. O’Connell ,23 Afshin Parsa,23

Iris M. Heid,5,37 Andrew D. Paterson,19,93 Ian H. de Boer,81 Olivier Devuyst,14

Jozef Lazar,94 Karlhans Endlich,72 Katalin Susztak,12 Johanne Tremblay,95 Pavel Hamet,95

Howard J. Jacob,7 Carsten A. Böger,6 Caroline S. Fox,9,96 Cristian Pattaro,13 andAnna Köttgen3,10

Genome-wide Association StudiesIdentify Genetic Loci Associated WithAlbuminuria in DiabetesDiabetes 2016;65:803–817 | DOI: 10.2337/db15-1313

Elevated concentrations of albumin in the urine, albumin-uria, are a hallmark of diabetic kidney disease and areassociated with an increased risk for end-stage renaldisease and cardiovascular events. To gain insight intothe pathophysiological mechanisms underlying albu-minuria, we conducted meta-analyses of genome-wideassociation studies and independent replication in up to

5,825 individuals of European ancestry with diabetesand up to 46,061 without diabetes, followed by func-tional studies. Known associations of variants in CUBN,encoding cubilin, with the urinary albumin-to-creatinineratio (UACR) were confirmed in the overall sample(P = 2.4 3 10210). Gene-by-diabetes interactions weredetected and confirmed for variants in HS6ST1 and near

Diabetes Volume 65, March 2016 803

GENETIC

S/G

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ROTEOMIC

S/M

ETABOLOMIC

S

RAB38/CTSC. Single nucleotide polymorphisms at theseloci demonstrated a genetic effect on UACR in indi-viduals with but not without diabetes. The change inthe average UACR per minor allele was 21% for HS6ST1(P = 6.33 10–7) and 13% for RAB38/CTSC (P = 5.83 1027).Experiments using streptozotocin-induced diabeticRab38 knockout and control rats showed higher

urinary albumin concentrations and reduced amountsof megalin and cubilin at the proximal tubule cell surfaceinRab38 knockout versus control rats. Relative expressionof RAB38 was higher in tubuli of patients with diabetickidney disease compared with control subjects. The lociidentified here confirm known pathways and highlightnovel pathways influencing albuminuria.

1Institute for Community Medicine, University Medicine Greifswald, Greifswald,Germany2German Center for Cardiovascular Research (DZHK), partner site Greifswald,Greifswald, Germany3Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health,Baltimore, MD4Institute of Genetics and Biophysics, “Adriano-Buzzati Traverso,” ConsiglioNazionale delle Ricerche, Naples, Italy5Department of Genetic Epidemiology, Institute of Epidemiology and PreventiveMedicine, University of Regensburg, Regensburg, Germany6Department of Nephrology, University Hospital Regensburg, Regensburg, Germany7Department of Physiology, Medical College of Wisconsin, Milwaukee, WI8Preventive Medicine, Brigham and Women’s Hospital, Boston, MA9National Heart, Lung, and Blood Institute’s Framingham Heart Study and the Centerfor Population Studies, Framingham, MA10Renal Division, Medical Center, University of Freiburg, Freiburg, Germany11Department of Life and Reproduction Sciences, University of Verona, Verona, Italy12Renal-Electrolyte and Hypertension Division, University of Pennsylvania, Phil-adelphia, PA13Center for Biomedicine, European Academy of Bozen/Bolzano (EURAC), affil-iated to the University of Lübeck, Bolzano, Italy14Institute of Physiology, Mechanisms of Inherited Kidney Disorders Group,University of Zürich, Zürich, Switzerland15Department of Neurology, Boston University School of Medicine, Boston, MA16Department of Biostatistics, Boston University School of Public Health, Boston, MA17Tufts Medical Center, Boston, MA18Department of Epidemiology and Preventive Medicine, Regensburg UniversityMedical Center, Regensburg, Germany19Genetics and Genome Biology Program, The Hospital for Sick Children ResearchInstitute, Toronto, Ontario, Canada20Loyola University Chicago, Maywood, IL21Division of Clinical Epidemiology and Aging Research, German Cancer ResearchCenter (DKFZ), Heidelberg, Germany22German Cancer Consortium (DKTK), Heidelberg, Germany23Department of Medicine, University of Maryland School of Medicine, Baltimore, MD24Icelandic Heart Association, Kópavogur, Iceland25Faculty of Medicine, University of Iceland, Reykjavik, Iceland26Human and Molecular Genetics Center, Medical College of Wisconsin,Milwaukee, WI27Renal Unit, Department of Medicine, University of Verona, Verona, Italy28Division of Genetic Epidemiology, Medical University of Innsbruck, Inns-bruck, Austria29Nephrology Division, Department of Specialties of Internal Medicine, GenevaUniversity Hospitals, Geneva, Switzerland30INSERM U-1018, Centre de recherche en épidémiologie et santé des pop-ulations (CESP) Team 5, Villejuif, France31UMRS 1018, Centre de recherche en épidémiologie et santé des populations(CESP) Team 5, Univ Paris Sud, Univ Versailles, St. Quentin, France32Fifth Department of Medicine, University Medical Centre Mannheim, Univer-sity of Heidelberg, Mannheim, Germany33First Department of Internal Medicine, Paracelsus Medical University/Salzburger Landeskliniken, Salzburg, Austria34Medical Research Council Human Genetics Unit, Institute of Genetics andMolecular Medicine, University of Edinburgh, Edinburgh, Scotland, U.K.

35INSERM U897, Bordeaux University, Institut de Santé Publique, d’Epidémio-logie et de Développement (ISPED), Bordeaux, France36Institute of Epidemiology II, Helmholtz Zentrum München, German ResearchCenter for Environmental Health, Neuherberg, Germany37Institute of Genetic Epidemiology, Helmholtz Zentrum München, GermanResearch Center for Environmental Health, Neuherberg, Germany38Vanderbilt University School of Medicine, Nashville, TN39University Heart Center Hamburg, Hamburg, Germany40German Center for Cardiovascular Research (DZHK e.V.), partner siteHamburg, Lübeck, Kiel, Germany41Medical Research Council Epidemiology Unit, University of CambridgeSchool of Clinical Medicine, Cambridge, U.K.42University Clinic for Nephrology, Hypertension and Clinical Pharmacology,Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland43Cardiovascular Health Research Unit, Departments of Epidemiology andMedicine, University of Washington, Seattle, WA44Diabetes Control and Complications Trial/Epidemiology of Diabetes Inter-ventions and Complications Research Group45Human Genetics Center, University of Texas Health Science Center, Houston, TX46Cardiology, Department of Specialties of Internal Medicine, Geneva UniversityHospitals, Geneva, Switzerland47Interfaculty Institute for Genetics and Functional Genomics, University MedicineGreifswald, Greifswald, Germany48Department of Internal Medicine, Lausanne University Hospital, Lausanne, Switzerland49Department of Internal Medicine, University Medical Center Groningen, Universityof Groningen, Groningen, the Netherlands50Division of Nephrology, Department of Internal Medicine and Medical Specialties,Columbus-Gemelli University Hospital, Catholic University, Rome, Italy51Institute of Epidemiology I, Helmholtz Zentrum München, German Research Cen-ter for Environmental Health, Neuherberg, Germany52Institute of Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany53Institute of Medical Statistics and Epidemiology, Technical University Munich,Munich, Germany54Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, GermanResearch Center for Environmental Health, Neuherberg, Germany55German Center for Diabetes Research (DZD), Neuherberg, Germany56Institute of Clinical Chemistry and Laboratory Medicine, University MedicineGreifswald, Greifswald, Germany57Department of Cardiology, University Medical Center Groningen, University ofGroningen, Groningen, the Netherlands58Centre for Population Health Sciences, University of Edinburgh, Edinburgh, Scot-land, U.K.59Nephrology, Department of Internal Medicine, University Medical Center Groningen,University of Groningen, Groningen, the Netherlands60Service of Medical Genetics, Centre Hospitalier Universitaire Vaudois and Univer-sity of Lausanne, Lausanne, Switzerland61Swiss Institute of Bioinformatics, Lausanne, Switzerland62INSERM UMR 1167 “Risk factors and molecular determinants of aging-relateddiseases,” Institut Pasteur de Lille, Lille, France63The George Institute for Global Health, University of Sydney, Camperdown, NewSouth Wales, Australia64Welch Center for Prevention, Epidemiology and Clinical Research, Baltimore, MD65Vanderbilt University School of Medicine, Nashville, TN

804 Genetic Association Study of Albuminuria Diabetes Volume 65, March 2016

Urinary albumin and serum creatinine are two biomarkersrecommended for the routine assessment of chronickidney disease (CKD) (1). Even at physiological rates ofglomerular filtration, small elevations in urinary albuminconcentrations are associated with an increased risk forCKD progression, end-stage renal disease (ESRD), cardio-vascular events, and cardiovascular and all-cause mortal-ity (2–4). Patients with diabetes are at a particularly highrisk for CKD and its sequelae: the prevalence of CKDamong individuals with diabetes is .40% compared with;10% in the general U.S. adult population (5), and thepresence of CKD is an important contributor to the excessmortality in diabetes (6). The appearance of significantamounts of albumin in the urine (albuminuria) is a hall-mark of diabetic kidney disease (DKD), the incidence ofwhich continues to rise along with type 2 diabetes world-wide (7). Residual diabetes-related microvascular risk rep-resents an important challenge even in treated individuals(8), and DKD remains the leading cause of ESRD. No neweffective treatments for DKD have been approved in morethan two decades (9), highlighting the importance to betterunderstand its underlying mechanisms.

Using genome-wide association study (GWAS) meta-analysis in general population cohorts, we previouslyidentified a missense single nucleotide polymorphism

(SNP) in the gene encoding cubilin (CUBN) in associationwith the urinary albumin-to-creatinine ratio (UACR) (10).CUBN is currently the only genome-wide significant locusfor UACR. However, this variant explains only a smallfraction of the previously reported heritability of albu-minuria, ranging from 0.2 to 0.46 in the general popula-tion and in those with diabetes (11–13), suggesting thatadditional genetic variants remain to be found. Here wereport the results of a GWAS meta-analysis of albumin-uria traits in the general population performed in almosttwice the sample size of our previous study (10), with aspecial focus on those with diabetes, replication in addi-tional independent individuals, and follow-up investiga-tions in human tissues and a genetically modified animalmodel of diabetes.

RESEARCH DESIGN AND METHODS

Study PopulationsOur study was based on 30 discovery and replicationstudies mostly from the general population, with theexception of Action in Diabetes and Vascular Disease:Preterax and Diamicron MR Controlled Evaluation(ADVANCE) and Genetics of Diabetic Nephropathy(GENDIAN), which enrolled exclusively individuals withtype 2 diabetes, totaling 67,452 participants of European

66Laboratory of Epidemiology and Population Sciences, Intramural Research Pro-gram, National Institute on Aging, National Institutes of Health, Bethesda, MD67Clinical Research Branch, National Institute on Aging, Baltimore, MD68 The George Institute for Global Health, Nuffield Department of PopulationHealth, University of Oxford, Oxford, U.K.69Service of Nephrology, Lausanne University Hospital, Lausanne, Switzerland70University Institute of Social and Preventive Medicine, Centre Hospitalier Uni-versitaire Vaudois and University of Lausanne, Épalinges, Switzerland71Department of Nephrology, University Hospital Regensburg, Regensburg, Germany72Institute of Anatomy and Cell Biology, University Medicine Greifswald, Greifswald,Germany73Human Genetics, Wellcome Trust Sanger Institute, Hinxton, U.K.74Department of Haematology, University of Cambridge, Cambridge, U.K.75Croatian Centre for Global Health, Faculty of Medicine, University of Split, Split,Croatia76Center for Thrombosis and Hemostasis (CTH), University Medical Center of theJohannes Gutenberg, University Mainz, Mainz, Germany77Preventive Cardiology and Preventive Medicine, Department of Medicine 2,University Medical Center of the Johannes Gutenberg, University Mainz, Mainz,Germany78German Center for Cardiovascular Research (DZHK), Mainz, Germany79Institute of Physiology, University Medicine Greifswald, Greifswald, Germany80Clinic for Prosthetic Dentistry, Gerostomatology and Material Science, Uni-versity Medicine Greifswald, Greifswald, Germany81Kidney Research Institute, Department of Medicine, University of Washing-ton, Seattle, WA82Genetics of Obesity and Related Metabolic Traits Program, The CharlesBronfman Institute for Personalized Medicine, The Mindich Child Health andDevelopment Institute, The Icahn School of Medicine at Mount Sinai, NewYork, NY83Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland84Kidney and Hypertension Section, Joslin Diabetes Center and Harvard MedicalSchool, Boston, MA

85Clinic for Internal Medicine A, University Medicine Greifswald, Greifswald,Germany86Institute for Human Genetics, Hannover Medical School, Hannover, Germany87Hannover Unified Biobank, Hannover Medical School, Hannover, Germany88Institute of Medical Biochemistry and Molecular Biology, University Medi-cine Greifswald, Greifswald, Germany89Abteilung Innere II, Universitätsklinikum Ulm, Ulm, Germany90Deutsches Herzzentrum München, Technische Universität München, Munich,Germany91German Centre for Cardiovascular Research (DZHK), partner site MunichHeart Alliance, Munich, Germany92University Institute of Social and Preventive Medicine, Centre HospitalierUniversitaire Vaudois and University of Lausanne, Lausanne, Switzerland93Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario,Canada94Department of Dermatology, Medical College of Wisconsin, Milwaukee, WI95Centre de Recherche du Centre hospitalier de l’Université de Montréal(CRCHUM), Université de Montréal, Montreal, Quebec, Canada96Division of Endocrinology, Brigham and Women’s Hospital and HarvardMedical School, Boston, MA

Corresponding authors: Anna Köttgen, anna.koettgen@uniklinik-freiburg.de,Alexander Teumer, ateumer@uni-greifswald.de, Caroline S. Fox, foxca@nhlbi.nih.gov, and Cristian Pattaro, cristian.pattaro@eurac.edu.

Received 21 September 2015 and accepted 25 November 2015.

This article contains Supplementary Data online at http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db15-1313/-/DC1.

A.Te., A.Ti., R.S., M.G., and N.C.Y. contributed equally to this work.

H.J.J., C.A.B., C.S.F., C.P., and A.K. provided joint oversight of this work.

© 2016 by the American Diabetes Association. Readers may use this article aslong as the work is properly cited, the use is educational and not for profit, andthe work is not altered.

diabetes.diabetesjournals.org Teumer and Associates 805

ancestry across the different analyses (up to 7,787 withdiabetes in discovery and replication). The study charac-teristics, including the distribution of albuminuria anddiabetes, are reported in Supplementary Table 1. Studyprotocols were approved by each local institutional reviewboard or ethics committee, and all human participantsgave written informed consent.

Phenotype Definitions and Analytical StrategyThe measurement of urinary albumin and creatinine ineach study is reported in Supplementary Table 2. Urinaryalbumin values below the detection limit of the used as-says were set to the lower limit of detection. Rather thanusing urinary albumin, the UACR was calculated as uri-nary albumin/urinary creatinine (mg/g) to account fordifferences in urine concentration. Microalbuminuria(MA) was defined as UACR .25 mg/g in women and.17 mg/g in men (10). Diabetes was defined as fastingglucose $126 mg/dL, nonfasting glucose $200 mg/dL, ortreatment for diabetes, or by self-report if this informa-tion was not available. Across studies, we evaluated twotraits, UACR and MA, and performed four GWAS meta-analyses: MA and UACR in the overall sample, as well asUACR—a continuous trait with higher statistical power—separately among those with and without diabetes.Diabetes-stratified genome-wide association analyses ofMA were not performed due to limited sample size. De-tailed information on the design, genotyping, imputation,and data management of each study is provided in Sup-plementary Tables 2 and 3.

Discovery Meta-Analysis, Replication, and PowerStringent quality control of the genetic data was per-formed at the individual study level and again at themeta-analysis level using state-of-the-art methods. Miss-ing genotypes were imputed using the HapMap referencepanels in 19 studies and the 1000 Genomes referencepanels in two studies. Details of genotyping, imputationsoftware, reference panels, and quality filters in eachstudy are reported in Supplementary Table 3.

All GWAS were performed following a standardizedanalysis protocol. In each study, the natural logarithm ofUACR was taken. Subsequently, sex-specific residuals wereobtained from linear regression models of ln (UACR) onage and study-specific covariates, including study centerand genetic principal components, to adjust for possiblepopulation stratification, if applicable. The continuoussex-specific residuals were then combined and used as thedependent variable that was regressed on imputed allelicdosages for each SNP in the GWAS.

Before the meta-analyses, all study-specific GWASsummary files underwent quality control using theGWAtoolbox (14). Genomic-control (GC) (15) correctionwas applied when the GC factor was .1. Inverse varianceweighted fixed-effects meta-analyses were conducted us-ing METAL (16). The I2 statistic was used to evaluatebetween-study heterogeneity (17). All meta-analyses

were performed in duplicate by two independentresearchers.

After meta-analysis, SNPs with average minor allelefrequency (MAF) ,0.01 were excluded, and another GCcorrection was applied. There were 2,191,945 SNPs withaverage MAF .0.05 and present in .50% of the studies,which were then clustered based on correlation (linkagedisequilibrium pruning using r2 # 0.2) with the respectiveindex SNP (the SNP with the lowest P value) within win-dows of 6 1 MB to identify independent SNPs with sug-gestive association (P , 1025) in one or more of the fouranalyses.

Replication testing was then performed for signals thatwere genome-wide significant (P , 5 3 1028) in any anal-ysis or that showed suggestive association among thosewith diabetes, motivated by the clinical importance ofDKD and the stronger association of the known and vali-dated CUBN variant on UACR among those with diabetes(18). Replication was defined as a one-sided P value ,0.05in the meta-analysis of independent replication studies.Five of the nine studies that contributed to replicationused imputed dosage, and four studies performed replica-tion genotyping of the index SNPs. A meta-analysis ofthe replication results was performed. The double GC-corrected results from the discovery meta-analysis and theresults of the nine replication studies were meta-analyzedto obtain the overall statistical significance. Unless statedotherwise, all reported P values are two-sided.

Assuming that associated SNPs explain a respective0.6% and 0.5% of the UACR variance in diabetes (Table 1),there was 95% and 91% power, respectively, to repli-cate the seven suggestive loci from the discovery stagein an additional 1,800 samples with a one-sided P valueof ,0.05.

Additional Analyses to Characterize Novel LociReplicated SNPs were further evaluated even in theabsence of genome-wide significance because, in additionto the significant replication P value, the low heterogene-ity across cohorts and the biological plausibility of theRAB38 locus further increased confidence in the findings.The SNPs were evaluated in the Diabetes Control andComplications Trial (DCCT)/Epidemiology of Diabetes In-terventions and Complications (EDIC) study for associa-tion with a primary clinical end point defined as timefrom the DCCT baseline until time to persistent MA ora secondary end point of time to incident albumin excre-tion rate .300 mg/24 h or ESRD (10). Time to outcomedevelopment or censoring was determined as the numberof visit years from the DCCT baseline up to and includingthe 12th year of EDIC follow-up. Subjects with persistentMA at DCCT baseline and DCCT year 1 were excludedfrom analyses of that outcome.

Epigenomic map analyses were performed, as describedpreviously (19), by using data from human kidney andkidney proximal tubule epithelial cells that can be accessedat Gene Expression Omnibus (GSE49637).

806 Genetic Association Study of Albuminuria Diabetes Volume 65, March 2016

Genetic associations with additional renal functiontraits, estimated glomerular filtration rate (eGFR), andCKD, were evaluated based on results from GWAS meta-analysis of the corresponding traits within the CKDGenConsortium (C.P., personal communication).

Gene Expression Analyses in Human TissuesQuantification of transcript abundance in microdissectedfractions of human glomeruli and tubuli from surgicalnephrectomies, living allograft donors, and portions ofdiagnostic kidney biopsies (20) was performed using RNAsequencing. Tissue from different renal compartmentswas separated using microdissection, homogenized, andstored at 280°C. Total RNA of human proximal tubulefractions (n = 256) and glomerular cells (n = 48) wasisolated using the RNeasy Mini Kit (Qiagen) accordingto the manufacturer’s instructions. RNA quality wasassessed with the Agilent Bioanalyzer 2100, and RNApreparations exhibiting RNA integrity number scores.7 were used for cDNA synthesis (library preparationat DNA Sequencing Core at UT Southwestern MedicalCenter). In short, 1 mg total RNA was used to isolatepoly A purified mRNA using the Illumina TruSeq RNAPreparation Kit. Single-end 100 bp sequencing was per-formed, and the annotated RNA counts (fastq) were cal-culated by CASAVA 1.8.2 (Illumina). Reads were mappedto the reference genome (National Center for Biotechnol-ogy Information build 37, hg19) using Spliced TranscriptsAlignment to a Reference (STAR). Reads per kilobase oftranscript per million mapped for HS6ST1 and RAB38were compared between glomerular and tubular fractionsusing a two-sided t test.

Comparison of candidate gene expression between casesubjects with DKD proven by biopsy specimen andhealthy control subjects was based on publicly availablemicroarray data from human microdissected glomeruliand tubuli (Gene Expression Omnibus, GSE 30122) (20).Raw data were analyzed using the R package “Affy” ver-sion 1.44.0, and expression levels were normalized usingrobust multiarray average. Transcript abundance between

patients and control subjects was compared using two-sided t tests; statistical significance was defined as P ,8.3 3 1023 (a = 0.05 corrected for six comparisons).

Studies of Rab38 in RatsTo better understand the association of RAB38 with albu-minuria in diabetes, we studied genetically modified ratmodels of diabetes. Eight Rab38 knockout (KO) rats on afawn-hooded hypertensive (FHH) background, seven ratstransgenic for the wild-type Brown Norway rat Rab38allele, and seven congenic rats were generated and raisedas described previously (21–23). Rab38 KO rats did notexpress the protein (22). These references also describethe recording of blood pressure and the measurement ofglucose and albuminuria. Diabetes was induced by admin-istering streptozotocin (STZ) (STZ; 50 mg/kg i.p.; Sigma-Aldrich, St. Louis, MO) to 9-week-old male rats.

Paraffin blocks of rat kidney samples were sectioned(6-mm thickness) with a Leica RM2255 rotary microtome(Thermo-Fisher Scientific, Waltham, MA) on SuperfrostPlus glass slides (12-550-15, Thermo-Fisher Scientific).Before staining, slides were deparaffinized in changes ofCitriSolv (22-143-975, Thermo-Fisher Scientific) and 70%isopropanol. Antigen retrieval was accomplished by incu-bating in sodium citrate buffer (1.8% 0.1 mol/L citric acid,8.2% 0.1 mol/L sodium citrate, in distillated water, pH6.0) in a rice cooker for 30 min. Slides were blocked withPBS blocking buffer (1% BSA, 0.2% nonfat dry milk inPBS) for 30 min and stained with primary antibodiesspecific for megalin or cubilin diluted in blocking bufferovernight at 4°C. Sheep anti-megalin and rabbit anti-cubilinwere provided by Dr. P. Verroust, INSERM, Paris, France.After two washes in 0.1% Tween 20 (v/v in PBS), slideswere incubated with corresponding fluorophore-conjugatedsecondary antibodies (Invitrogen), diluted in blocking bufferat room temperature for 1 h, and counterstained with10 mmol/L Hoechst 33342 (Molecular Probes-Invitrogen,H1399). Slides were subsequently mounted in Prolong GoldAntifade reagent (Invitrogen), and images were acquired witha Leica SP5 confocal laser scanning microscope (Center for

Table 1—Replicated SNP associations with UACR in individuals with diabetes

Sample size (n) Effect on log(UACR) mg/g SE P value I2 %

rs649529, RAB38Discovery 5,825 20.15 0.03 9.3 3 1026 0Replication 1,962 20.12 0.05 0.02 0Combined 7,787 20.14 0.03 5.8 3 1027 0

rs13427836, HS6ST1Discovery 5,509 0.20 0.04 6.1 3 1026 10Replication 1,890 0.16 0.07 0.03 58Combined 7,399 0.19 0.04 6.3 3 1027 30

For both variants, the effect of each additional copy of the minor allele (T) on UACR was modeled in an additive fashion. I2 is providedas a measure of heterogeneity across studies. Imputation quality ranged from 0.41 to 1.0 for rs649529 and from 0.44 to 1.0 forrs13427836. The variants were directly genotyped in four of the replication studies, with a call rate ranging from 0.98 to 1.0 for rs649529and from 0.99 to 1.0 for rs13427836. The estimated proportion of explained variance in UACR among those with diabetes is 0.6% forrs649529 and 0.5% for rs13427836, using the formula 2 3 MAF 3 (1-MAF) 3 effect2/var(log[UACR]), based on the combined effectestimates from Table 1 and the phenotypic variance in the large population-based ARIC Study.

diabetes.diabetesjournals.org Teumer and Associates 807

Microscopy and Image Analysis, University of Zurich) equip-ped with a Leica APO 363 NA 1.4 oil immersion objective.

All experiments were performed in compliance withNational Institutes of Health Guide for Care and Use ofLaboratory Animals, and all used protocols were approvedby the local institutional animal care and use committee.

RESULTS

Discovery of Genomic Loci Associated WithAlbuminuria TraitsThe discovery GWAS meta-analyses for the four traitsincluded up to 20 studies and up to 54,450 individuals pertrait. The median UACR in the 20 individual studies thatcontributed to the UACR meta-analysis ranged from 2.5to 15.6 mg/g. Across all studies, the mean proportion ofwomen was 53%, and the median of average age was 57years. The prevalence of diabetes in the population-basedstudies ranged from 1 to 14% (Supplementary Table 1).

There was no evidence of systematic biases influencingthe genome-wide association results, as indicated by lowgenomic control parameters (Supplementary Fig. 1). OnlySNPs in the previously identified CUBN locus showed genome-wide significant association with both UACR (P = 2.43 10210,Supplementary Table 4 and Supplementary Fig. 2) andMA (P = 1.3 3 10210, Supplementary Table 5 and Supple-mentary Fig. 2). The effect of the minor C allele of the indexSNP rs10795433 on logarithmic UACR values was fourfoldlarger among 5,825 individuals with diabetes (0.19 log[mg/g],P = 2.0 3 1025) compared with 46,061 individuals withoutdiabetes (0.045 log[mg/g], P = 8.7 3 1026; P = 8.2 3 1024

for difference). This corresponds, for each additional C allele,to a 5% higher geometric mean of UACR (exp[0.045]) inindividuals without diabetes compared with 21% higheraverage UACR in those with diabetes (exp[0.19]).

Suggestive associations were identified for all fouranalyses (Supplementary Tables 4–7 and regional associ-ation plots in Supplementary Fig. 3). Among the clinicallyimportant group of individuals with diabetes, seven geno-mic loci contained one or more SNPs showing a suggestiveassociation with UACR. These were exclusively identified inthe meta-analysis of individuals with diabetes and mappedinto or near HS6ST1, CNTN4, KBTBD8, TFAP2B/PKHD1,CHN2, WDR11/FGFR2, and RAB38/CTSC (SupplementaryTable 7). Following our analytical strategy, we selected theindex SNP in each of these seven regions for follow-upamong up to 1,962 independent individuals with diabetes.

The Supplementary PDF document contains the QQand Manhattan plots of all GWAS meta-analyses, the re-gional association plots, tables with cohort descriptions,and association results of SNPs at P , 1025. Results fromdiscovery GWAS meta-analysis are publicly available athttp://fox.nhlbi.nih.gov/CKDGen/.

Replication Analyses Implicate RAB38/CTSC andHS6ST1 as Novel Loci for UACR in DiabetesThe replication analyses included nine studies and up to1,962 individuals with diabetes. The median UACR across

replication studies ranged from 3.8 to 14.5 mg/g. Themean proportion of women was 49%, and the median ofaverage age was 55 years. For the seven SNPs testedfor replication (Supplementary Table 8), we assessedwhether the one-sided P value was ,0.05 in the com-bined replication studies (see RESEARCH DESIGN AND METHODS).This was the case for two SNPs: intergenic rs649529upstream of RAB38/downstream of CTSC on chromo-some 11q14 (Fig. 1A) and the intronic variant rs13427836in HS6ST1 on chromosome 2q21 (Fig. 1B). As illustratedin Fig. 1C, each additional copy of the minor T allele ofrs649529 at RAB38/CTSC was consistently associatedwith lower UACR among the 5,825 individuals in thediscovery and 1,962 in the replication cohorts (combinedP = 5.8 3 1027) (Table 1), with no evidence of heteroge-neity across cohorts (I2 = 0%). This effect correspondedto 13% lower geometric mean of UACR per copy of theT allele. Similarly, rs13427836 in HS6ST1 showed consis-tent effects across cohorts (combined P = 6.3 3 1027)(Table 1), with each copy of the T allele associated withan ;21% higher mean UACR but moderate heterogeneity(I2 = 29.9%) (Fig. 1D).

The association of rs649529 near RAB38/CTSC andrs13427836 in HS6ST1 was not found in individualswithout diabetes (P = 0.64 and P = 0.38, respectively)(Table 2). Differences in the association with UACRamong those with and without diabetes were significant(t test for difference: P = 6.9 3 1026 for rs649529 andP = 1.7 3 1025 for rs13427836). Effects for the indexvariant in CUBN are provided for comparison. We alsoevaluated the association of the replicated SNPs withMA in the setting of diabetes. Information was obtainedfrom a subset of studies with sufficiently high numbersof individuals with diabetes and MA (n = 2,552; Athero-sclerosis Risk in Communities Study [ARIC], Cardiovas-cular Health Study [CHS], Cohorte Lausannoise [CoLaus],European Prospective Investigation into Cancer and Nu-trition [EPIC], Framingham Heart Study [FHS], Koopera-tive Gesundheitsforschung in der Region Augsburg [KORA]F3, KORAF4, and Study of Health in Pomerania [SHIP]).Across cohorts, the odds ratio for MA for each copyof the minor allele was 0.84 for rs649529 near RAB38(P = 0.019) and 1.39 for rs13427836 in HS6ST1 (P = 7.83 1024), consistent with the direction of the SNP effectson UACR.

Characterization of Genetic Effects by Markers ofKidney Function and DiabetesWe next investigated whether the gene-by-environmentinteraction was also observed for the eGFR, anothermeasure of kidney function, and/or diabetes or glycemictraits. No statistically significant associations were foundbetween rs649529, rs13427836, rs10795433, and eGFRin those with or without diabetes (Table 2) and no asso-ciations were found with CKD. There were also no statis-tically significant associations of these variants with type 2diabetes, fasting blood glucose, or plasma hemoglobin

808 Genetic Association Study of Albuminuria Diabetes Volume 65, March 2016

A1c concentrations (Table 2), indicating that the observedassociations pertain to albuminuria in the setting of di-abetes rather than to diabetes or impaired glucose metab-olism per se. A comprehensive search in the NationalHuman Genome Research Institute GWAS Catalog (24)did not reveal any significant associations between thetwo validated SNPs or their proxies with other diseasesor traits.

Variant EvaluationUsing publicly available data of genetic effects on geneexpression (25), we found an association in cis betweenrs649529 and transcript levels of RAB38 (P = 5.4 3 1026)and the neighboring CTSC (P = 7.6 3 1027), consistentwith a regulatory effect of this variant in whole blood.Corresponding data for kidney-specific tissues are cur-rently not available, but we used epigenetic maps generated

Figure 1—Overview of associated genomic loci at RAB38/CTSC and HS6ST1 and consistent association with albuminuria in diabetes acrossthe contributing studies. A: Regional association plot of the RAB38/CTSC locus on chromosome 11. B: The T allele at rs649529 is associatedwith lower UACR across discovery and replication studies. C: Regional association plot of the HS6ST1 locus on chromosome 10. D: TheT allele of intronic rs13427836 is associated with higher UACR across discovery and replication studies. The solid squares indicate the meandifference and are proportional to the weights used in the meta-analysis. The solid vertical line indicates no effect. The diamond indicates theweighted mean difference, and the lateral tips of the diamond indicate the associated CIs. The horizontal lines represent the 95% CI.

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from human adult kidney tissue (19) (see RESEARCHDESIGN AND

METHODS) to further examine the regulatory potential of in-dex SNPs. The intronic index SNP in HS6ST1 and severalproxies mapped into enhancer regions. Similarly, the CUBNindex variant rs10795433 mapped into an intronic enhancerregion. The region in which the index variant at RAB38/CTSC is located was annotated as not mapped/repressedin these cells, thus preventing further examination. All prox-ies in strong linkage disequilibrium with these three indexSNPs (r2 . 0.6, 1000G v5 reference panel) (26) wereintronic (CUBN and HS6ST1) or intergenic (RAB38/CTSC).

Clinical Characterization Including Gene Expression ofReplicated LociTo evaluate target tissues within the kidney, we charac-terized the identified loci using data for tissue-specific

gene expression. Clinical characterization was conductedusing data from patients with DKD and healthy controlsubjects (27) and a prospective study of individuals withtype 1 diabetes (28).

We used publicly available data (27) to compare rela-tive expression of RAB38, CTSC, and HS6ST1 betweenpatients with DKD confirmed by biopsy specimen andhealthy control subjects (see RESEARCH DESIGN AND METHODS).After multiple testing correction, only RAB38 expressionlevels were significantly different, with higher expres-sion in tubuli of DKD patients compared with controlsubjects (P = 1.3 3 1024) (Fig. 2A). We also used RNAsequencing data from microdissected human kidneysamples to quantify RAB38 and HS6ST1 expression inhuman glomeruli and tubuli. HS6ST1 showed higher ex-pression levels than RAB38, and both genes showed

Table 2—Replicated SNP associations with additional kidney function and diabetes-related traits

Trait n Effect (OR) SE P value P difference*

rs649529, RAB38/CTSC

UACR, diabetes; log(mg/g) 7,787 20.14 0.03 5.8 3 1027

6.9 3 1026

UACR, no diabetes; log(mg/g) 45,094 20.004 0.008 0.64

eGFRcrea, diabetes; log(mL/min/1.73 m2) 11,527 0.003 0.004 0.462.8 3 1021

eGFRcrea, no diabetes; log(mL/min/1.73 m2) 118,427 20.001 0.001 0.59

CKD (eGFR ,60 mL/min/1.73 m2) 118,114 (1.01) 0.02 0.57

Type 2 diabetes 63,390 (1.02) 0.02 0.32

Fasting glucose (mmol/L) 46,186 0.003 0.006 0.65

HbA1c (%) 46,368 0.004 0.004 0.31rs13427836, HS6ST1

UACR, diabetes; log(mg/g) 7,399 0.19 0.04 6.3 3 1027

1.7 3 1025

UACR, no diabetes; log(mg/g) 34,830 0.010 0.012 0.38

eGFRcrea, diabetes; log(mL/min/1.73 m2) 11,092 0.008 0.006 0.131.3 3 1021

eGFRcrea, no diabetes; log(mL/min/1.73 m2) 114,247 0.000 0.001 0.94

CKD (eGFR ,60 mL/min/1.73m2) 113,612 (0.97) 0.02 0.23

Type 2 diabetes 63,390 (1.00) 0.03 0.94

Fasting glucose (mmol/L) 46,186 20.005 0.004 0.22

HbA1c (%) 46,368 0.003 0.005 0.61rs10795433, CUBN†

UACR, diabetes; log(mg/g) 5,825 0.19 0.04 2.0 3 1025

8.2 3 1024

UACR, no diabetes; log(mg/g) 46,061 0.045 0.01 8.7 3 1026

eGFRcrea, diabetes; log(mL/min/1.73 m2) 11,522 0.007 0.005 0.181.9 3 1021

eGFRcrea, no diabetes; log(mL/min/1.73 m2) 118,299 0.0007 0.001 0.61

CKD (eGFR ,60 mL/min/1.73 m2) 118,121 (1.04) 0.02 0.08

Type 2 diabetes 63,390 (1.00) 0.03 0.88

Fasting glucose (mmol/L) 46,186 20.003 0.005 0.52

HbA1c (%) 46,368 20.002 0.005 0.73

DM, diabetes mellitus; eGFRcrea, GFR estimated by serum creatinine levels; OR, odds ratio. Effects represent the change in traitassociated with each additional copy of the minor allele for each of the SNPs. For continuous traits, units are provided; the effect forbinary outcomes, shown in parentheses, represents the OR. Except for UACR in diabetes, estimates refer to the discovery samples ofthe respective trait and to the published resources for the glycemic traits. Fasting glucose and HbA1c were evaluated among individualsfree of diabetes. For the kidney traits, P values and SEs are corrected using genomic control. *P value for difference from a two-samplet test: t = (effectDM 2 effectnonDM)/(SEDM

2 + SEnonDM2)0.5 which, for large sample sizes is distributed as a normal (0,1). The correlation

between effectDM and effectnonDM is assumed to be 0. Associations with type 2 diabetes were tested using the publicly availablesummary statistics dataset from the DIAGRAM (DIAbetes Genetics Replication And Meta-analysis) Consortium (12,171 case subjectsand 56,862 control subjects) (40). Associations with fasting glucose and plasma hemoglobin A1c concentrations were evaluated usingthe publicly available results from the MAGIC Consortium (www.magicinvestigators.org) (41,42). †UACR effect estimates for UACR indiabetes for CUBN are provided from the discovery stage.

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higher expression in tubuli than in glomeruli (Fig. 2B).The difference between tubular and glomerular expres-sion was more pronounced for RAB38 (P = 1.1 3 1028)than for HS6ST1 (P = 0.015).

To investigate whether the effect of the replicatedSNPs extended to kidney disease progression in thesetting of type 1 diabetes, the SNPs were tested for asso-ciation with incident MA (268 case subjects, primaryend point) and a combined end point of time to ma-croalbuminuria or ESRD (133 case subjects, second-ary end point) among up to 1,304 participants withtype 1 diabetes in the DCCT/EDIC study (28). NeitherSNP showed a significant association (SupplementaryTable 9).

Diabetic Rab38 KO Rats Show Increased UrinaryAlbumin ExcretionWe aimed to further substantiate our findings by obtain-ing experimental support. We focused on the examinationof RAB38 because it was the gene implicated by highergene expression in tubuli of DKD patients compared withcontrol subjects and because previous studies of Rab38KO and transgenic rats have confirmed its role in albu-minuria in FHH rats and highlighted a role in tubularalbumin reuptake (21,22). We thus examined these animalsin the setting of diabetes as outlined in Fig. 3A. Injectionof STZ in 9-week-old rats successfully induced diabetes inall strains (Fig. 3B). Blood glucose rose from normal valuesbefore injection of STZ (congenic, 2056 3 mg/dL; transgenic,

Figure 2—RAB38 and HS6ST1 expression across kidney tissues. A: Comparison of RAB38 and HS6ST1 expression (microarray) in tubuliand glomeruli of patients with DKD and control subjects shows significantly higher RAB38 expression in tubuli of DKD patients than intubuli of control subjects (significance threshold 0.05/6 = 8.3 3 1023 for investigating RAB38, CTSC, and HS6ST1 in tubuli and glomeruli).CTSC expression was not significantly different between DKD case subjects and control subjects in tubuli (P = 0.11) or glomeruli (P = 0.03).Expression levels are shown as robust multiarray average–processed gene intensity values. B: RAB38 and HS6ST1 transcript abundancequantified from RNA sequencing (RNAseq) is detected at high levels in human tubuli but also in glomerular cells. Transcripts were quantifiedby reads per kilobase of transcript per million mapped (RPKM). The error bars in A and B correspond to the SEM.

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227 6 11 mg/dL; KO, 198 6 7 mg/dL) to high values thatindicate severe hyperglycemia 1 week after STZ (congenic,422 6 35 mg/dL; transgenic, 406 6 27 mg/dL; KO, 420 621 mg/dL). Blood glucose levels remained high at age 11, 12,and 13 weeks and showed no significant differences amongstrains (Fig. 3B). There were no significant differences inmean arterial blood pressure among congenic, transgenic,and KO animals freely moving around the cage. All animalstrains showed a tendency toward decreased blood pressure3–4 weeks after injection of STZ (Fig. 3C).

As illustrated in Fig. 3D, Rab38 KO animals showed aprogressive increase in urinary albumin excretion that

became statistically significant 2 weeks after injection ofSTZ. At 4 weeks after injection, Rab38 KO animals had analbumin excretion of 79 6 14 mg/day, whereas albuminexcretion was only 286 8 mg/day in transgenic (P, 0.01)and 41 6 13 mg/day in congenic animals (P , 0.01).These data indicate that diabetic rats without Rab38 aremore susceptible to the development of albuminuriathan congenic and transgenic animals with functionalRab38 despite a similar degree of hyperglycemia in allanimals. Kidney sections obtained from a subset of an-imals showed a higher average glomerulosclerosis score(2.9 6 0.3) compared with congenic (2.2 6 0.1) and

Figure 3—Comparison of Rab38 congenic, transgenic, and KO rats after induction of diabetes. A: Experimental setup and timeline. BP,blood pressure measurement via radio telemetry; MC, metabolic cage. B: Comparison of blood glucose concentrations. C: Comparison ofmean arterial pressure. D: Comparison of urinary albumin concentrations. *P < 0.05, **P < 0.01 KO vs. transgenic, ##P < 0.01 KO vs.congenic. E: Expression of endocytic markers. Immunofluorescence staining for megalin (green, top panel) and cubilin (red, bottom panel)in kidneys from all three rat strains. Nuclei counterstained with DAPI (blue). Scale bar, 50 mm. Data are presented as mean 6 SEM. Theresults for blood pressure measurement, urinary albumin excretion, and blood glucose were analyzed by two-way ANOVA, followed by theTukey post hoc test.

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transgenic (2.2 6 0.1) rats (P , 0.05) (SupplementaryFig. 4), but differences were subtler than those observedfor urinary albumin excretion.

To further clarify how loss of Rab38 may lead to albu-minuria, we performed immunohistochemistry staining ofmegalin and cubilin, known to mediate albumin reuptakein the proximal tubulus, in kidney sections of all threeanimal strains. Cubilin and megalin were markedly re-duced at the luminal membrane of proximal tubular cellsin Rab38 KO rats compared with congenic and transgeniccontrol animals (Fig. 3E), consistent with a role of Rab38in regulating the abundance of cubilin and megalin at thecell surface. In contrast, there was no significant differ-ence in the number of structures positive for the lyso-somal marker LAMP1 among the three strains.

DISCUSSION

In this GWAS discovery meta-analysis of 2,191,945 SNPsin up to 54,450 participants of 20 studies, we replicatedthe association of the previously identified CUBN locusand UACR as well as MA at genome-wide significance andidentified several suggestive signals among individuals withdiabetes. Two of these loci, RAB38/CTSC and HS6ST1,showed evidence of independent replication, and RAB38was further supported by functional studies in a rat model.Our findings point to mechanisms in renal handling ofalbumin that associate with albuminuria in humans inthe setting of diabetes. They thus represent examples ofgene-by-diabetes interactions resulting in a complex traitthat manifests when environmental exposure and geneticsusceptibility variants occur together (29).

Not all individuals with diabetes develop DKD, sug-gesting that neither the presence of hyperglycemia norgenetic variants alone are sufficient to elicit the renaldamage that typically manifests itself as albuminuria indiabetes. Our observations therefore raise the question ofhow the diabetic environment may result in the manifes-tation of genetic effects on albuminuria. The lack ofassociation between both genetic variants and type 2diabetes or specific glycemic measures in humans indi-cates that their effects occur without influencing diabetesper se. This notion is further substantiated by the factthat diabetic Rab38 KO rats showed higher urinary albu-min concentrations compared with controls despite thepresence of similar blood glucose concentrations.

A difference between our observations in humans andrats is that the effect of genetic variation near RAB38 onalbuminuria was only found in humans with but not with-out diabetes, whereas Rab38 KO rats without diabetesalso progress to albuminuria (22). A potential explanationis that KO rats represent a null mutation, allowing for thegenetic component to take full effect without needingfurther aggravation by environmental factors. Conversely,many human susceptibility variants of complex traits donot result in a complete loss of function but instead are ofa regulatory nature. The effect of such variants may be-come apparent only upon an environmental challenge,

such that genetically determined alterations in renal albu-min handling could manifest themselves in the setting ofhyperglycemia and/or diabetes due to a number of mech-anisms that secondarily affect albumin reabsorption, in-cluding an increased load of filtered albumin due tohyperfiltration or impairment of the glomerular filter.Along these lines, our observation of significantly higherRAB38 transcript abundance in tubuli of DKD patientsthan control subjects may indicate an adaption of thetubular machinery for albumin reabsorption in this set-ting. Moreover, genetics effects of the index SNP at theCUBN locus on albuminuria were four times as large inindividuals with diabetes compared with those withoutdiabetes, supporting alterations of tubular albumin han-dling in the setting of diabetes.

RAB38 encodes a member of the small Rab GTPaseprotein family that regulates intracellular vesicle traffick-ing between organelles and is important in exo-, endo-,and transcytosis (30). Expression of Rab38 at themRNA and protein level was observed in proximal tubulecells of wild-type rats (31). FHH rats, a natural Rab38-nullmutation, show increased urinary albumin excretion with-out changes in glomerular permeability (21). In these an-imals, the expression of a Brown Norway Rab38 transgeneled to phenotypic rescue, and knockdown of Rab38 in aproximal tubule cell system significantly decreased albu-min endocytosis (22). Together, these observations sup-port an important role for the small Rab GTPase RAB38in the reabsorption of filtered albumin.

Impaired RAB38 function may lead to increased albu-min excretion via different mechanisms: altered intracellularvesicle transport may affect albumin reabsorption orrecycling of reabsorbed albumin back to the plasmamembrane (32). Alternatively, altered RAB38 function mayaffect the delivery of proteins required, such as cubilin ormegalin, for albumin endocytosis, the mechanism underly-ing albuminuria in Dent disease (33). Our experimental datashowing reduced abundance of cubilin and megalin in Rab38KO but not in control rats are consistent with the latterhypothesis. Finally, that impaired RAB38 function may di-rectly cause glomerular damage, in turn leading to increasedconcentrations of urinary albumin, is also conceivable.

Although the combined evidence from Rab38 KO ratsalong with the gene expression and GWAS data stronglyimplicate RAB38 as the gene underlying albuminuria inhumans, the intergenic index SNP mapped upstream ofRAB38 and downstream of CTSC and was associated withtranscript levels of both genes in whole blood. We cantherefore not exclude the possibility that CTSC may bethe causal gene underlying the observed associations orthat it contributes to the phenotype in addition to RAB38.CTSC encodes for a lysosomal cysteine protease. Raremutations in the gene cause autosomal-recessive Papillon-Lefèvre syndrome. No renal abnormalities have beenreported in affected patients (34), Ctsc KO mice do notshow kidney abnormalities (35), and the gene has not beenlinked to albuminuria or kidney disease.

diabetes.diabetesjournals.org Teumer and Associates 813

The other genomic locus associated with albuminuriain diabetes contains HS6ST1, encoding the enzyme hep-aran sulfate (HS) 6-O-sulfotransferase that catalyzes the6-O-sulfation of HS and heparin (36,37). HS are anionicside chains of HS proteoglycans, which are components ofbasement membranes, extracellular matrix, and cell sur-faces. Several studies have reported that inactivation orremoval of HS leads to proteinuria, and biopsy specimensfrom patients with diabetes revealed changes in HS sulfa-tion patterns compared with control subjects (38). Thus, agenetic variant altering the activity or abundance of theenzyme may lead to altered albuminuria. The underlyingmechanisms could be manifold, because HS has been re-ported to affect not only glomerular filtration but alsogrowth factor signaling, composition, and functions of theglomerular basement membrane as well as functions at theendothelial surface layer (38) and the proximal tubule (39).

Strengths of our study include its large sample size,specific examination of individuals with diabetes, andconsistent effects across a variety of studies underscoringthe relevance of our findings at the general population level.In addition, we performed careful characterization of areplicated finding through in vivo experiments in RAB38 KOand control rats that cannot, however, elucidate the exactmechanism by which genetic variation at this locus influ-ences albuminuria in humans.

Limitations include the fact that the replicated SNPsdid not achieve genome-wide significance, necessitatingfuture confirmation in even larger studies, and that wecould not assess allele-specific gene expression in humankidney tissues. We focused on European ancestry studyparticipants and mostly on individuals with type 2diabetes. Future studies should therefore examine theseassociations among individuals of additional ancestriesand in well-powered studies of patients with type 1diabetes. Although results were combined after study-specific analyses, biological variation in UACR and differ-ent urine collection and storage methods may haveresulted in increased variation and thus reduced statisti-cal power to reveal significant associations.

Additional studies are required to determine the causalvariants and the exact underlying molecular mechanismby which genetic variation at RAB38/CTSC and HS6ST1associates with albuminuria in humans. An elucidation ofthe underlying mechanisms and the contributions anddifferences of albuminuria of glomerular and tubular or-igin may improve our understanding of proteinuric kid-ney diseases in general but may be especially relevant toDKD, the most common cause of ESRD.

Acknowledgments and Funding. 3C Study: The work was madepossible by the generous participation of the control subjects, the patients, andtheir families. The authors thank Dr. Anne Boland, Centre National deGénotypage, for her technical help in preparing the DNA samples for analyses.This work was supported by the National Foundation for Alzheimer’s disease andrelated disorders, the Institut Pasteur de Lille, and the Centre National de

Génotypage. The Three-City (3C) Study was performed as part of a collaborationbetween INSERM, the Victor Segalen Bordeaux II University, and Sanofi-Synthé-labo. The Fondation pour la Recherche Médicale funded the preparation andinitiation of the study. The 3C Study was also funded by the Caisse NationaleMaladie des Travailleurs Salariés, Direction Générale de la Santé, MGEN, Institutde la Longévité, Agence Française de Sécurité Sanitaire des Produits de Santé,the Aquitaine and Bourgogne Regional Councils, Fondation de France, and thejoint French Ministry of Research/INSERM “Cohortes et collections de donnéesbiologiques” programme. Lille Génopôle received an unconditional grant fromEisai.ADVANCE Study: The genetic epidemiological work was funded by Prognomix,

Inc., and by grants from Genome Quebec and Canadian Institutes of HealthResearch (CIHR). The clinical study was managed by The George Institute forGlobal Health (University of Sydney, Sydney, New South Wales, Australia) withgrants received from Les Laboratoires Servier, France, and from the NationalHealth and Medical Research Council of Australia. The genotyping was performedat the genomic platform of Centre de Recherche du Centre hospitalier del’Université de Montréal (CRCHUM). The authors acknowledge the technical helpof Carole Long and Mounsif Haloui and the bioinformatic analyses performed byGilles Godefroid, François-Christophe Blanchet-Marois, and François Harvey(CRCHUM, Université de Montréal, Montreal, Quebec, Canada). Also acknowl-edged are the members of the genetic substudy of ADVANCE, Stephen Harrap(Department of Physiology, University of Melbourne, Melbourne, Victoria, Aus-tralia) and Michel Marre (Diabétologie, Endocrinologie, Nutrition at Hôpital Bichat,Paris, France). M.W. did not participate in animal experiments.AGES Study: The study has been funded by National Institutes of Health (NIH)

contracts N01-AG-1-2100 and HHSN271201200022C, the National Institute onAging Intramural Research Program, Hjartavernd (the Icelandic Heart Associa-tion), and the Althingi (the Icelandic Parliament). The study is approved by theIcelandic National Bioethics Committee, VSN: 00-063. The researchers areindebted to the participants for their willingness to participate in the study.Amish Study: The authors thank the Amish research volunteers for their long-

standing partnership in research and the research staff at the Amish ResearchClinic for their hard work and dedication. The study is supported by grants andcontracts from the NIH, including R01-AG-18728 (Amish Longevity Study), R01-HL-088119 (Amish Calcification Study), U01-GM-074518-04 (Pharmacogenomicsof Anti-Platelet Intervention [PAPI] Study), U01-HL0-72515-06 (Heredity andPhenotype Intervention [HAPI] Study), U01-HL0-84756, and K12-RR0-23250(University of Maryland Multidisciplinary Clinical Research Career DevelopmentProgram), the University of Maryland General Clinical Research Center, grantM01-RR-16500, the Baltimore Veterans Administration Medical Center GeriatricResearch Education and Clinical Center, and the Paul Beeson Physician FacultyScholars in Aging Research Program. Partial funding was also provided by theMid-Atlantic Nutrition Obesity Research Center (P30-DK0-72488).ARIC Study: The ARIC is carried out as a collaborative study supported by

National Heart, Lung, and Blood Institute contracts (HHSN268201100005C,HHSN268201100006C, HHSN268201100007C, HHSN268201100008C,HHSN268201100009C, HHSN268201100010C, HHSN268201100011C,and HHSN268201100012C), R01-HL0-87641, R01-HL-9367 and R01-HL0-86694,National Human Genome Research Institute contract U01-HG0-04402, and NIHcontract HHSN268200625226C. The authors thank the staff and participantsof the ARIC study for their important contributions. Infrastructure was partlysupported by grant number UL1-RR0-25005, a component of the NIH and NIHRoadmap for Medical Research. This work as well as Y.L. and A.K.were supported by the Emmy Noether Programme of the German ResearchFoundation (KO 3598/2-1 to A.K.).Baltimore Longitudinal Study of Aging (BLSA): BLSA was supported in part by

the Intramural Research Program of the NIH, National Institute on Aging.CHS: The CHS research reported in this article was supported by National

Heart, Lung, and Blood Institute contracts N01-HC-85079 through N01-HC-85086, N01-HC-35129, N01-HC-15103, N01-HC-55222, N01-HC-75150, N01-HC-45133, and grant numbers U01-HL0-80295 and R01-HL0-87652, with

814 Genetic Association Study of Albuminuria Diabetes Volume 65, March 2016

additional contribution from the National Institute of Neurological Disorders andStroke. A full list of principal CHS investigators and institutions can be found athttp://www.chs-nhlbi.org/pi. DNA handling and genotyping was supported in partby National Center for Research Resources grant M01-RR-00425 to the Cedars-Sinai General Clinical Research Center Genotyping core and National Institute ofDiabetes and Digestive and Kidney Diseases grant DK-063491 to the SouthernCalifornia Diabetes Endocrinology Research Center.

CoLaus Study: The CoLaus study received financial contributions fromGlaxoSmithKline, the Faculty of Biology and Medicine of Lausanne, and theSwiss National Science Foundation (33CSCO-122661).

CROATIA-Split Study: The CROATIA-Split study was supported throughgrants from the Medical Research Council U.K., and Ministry of Science,Education and Sport of the Republic of Croatia (No. 108-1080315-0302). Theauthors acknowledge the invaluable contributions of the recruitment team fromthe Croatian Centre for Global Health, University of Split, the administrative teamsin Croatia and Edinburgh, and the people of Split. The SNP genotyping for theSplit cohort was performed by AROS Applied Biotechnology, Aarhus, Denmark.

EPIC-Norfolk Study: The EPIC-Norfolk Study is supported by program grantsfrom the Medical Research Council, and Cancer Research U.K. The authorsacknowledge the contribution of the staff and participants of the EPIC-NorfolkStudy.

ESTHER Study: The authors thank all the individuals who took part in theESTHER (Epidemiological investigations of the chances of preventing, recognizingearly and optimally treating chronic diseases in an elderly population) study andall the researchers, clinicians, technicians, and administrative staff who haveenabled this work to be carried out. This work was supported in part by theBaden-Württemberg State Ministry of Science, Research and Arts, by the GermanFederal Ministry of Education and Research (grant number 01ET0717), and bythe CHANCES (Consortium on Health and Ageing: Network of Cohorts in Europeand the United States) project funded in the Seventh Framework Programme ofthe Directorate-General for Research and Innovation in the European Commission(grant number 242244).

Fenland Study: The Fenland study is funded by the Medical Research Council(MC_U106179471) and Wellcome Trust. The authors are grateful to all thevolunteers for their time and help and to the general practitioners and practicestaff for assistance with recruitment. The authors thank the Fenland StudyInvestigators, Fenland Study Co-ordination team, and the Epidemiology Field,Data and Laboratory teams.

Framingham Heart Study: This research was conducted in part using data andresources from the Framingham Heart Study of the National Heart, Lung, andBlood Institute of the NIH and Boston University School of Medicine. The analysesreflect intellectual input and resource development from the Framingham HeartStudy investigators participating in the SNP Health Association Resource (SHARe)project. This work was partially supported by the National Heart, Lung and BloodInstitute Framingham Heart Study (Contract No. N01-HC-25195) and its contractwith Affymetrix, Inc. for genotyping services (Contract No. N02-HL-6-4278). Aportion of this research used the Linux Cluster for Genetic Analysis (LinGA-II)funded by the Robert Dawson Evans Endowment of the Boston University Schoolof Medicine Department of Medicine and Boston Medical Center.

GENDIAN Study: The support of the physicians, the patients, and the staff ofthe Diabetes Zentrum Mergentheim (Head: Prof. Dr. Thomas Haak), the diabetesoutpatient clinic Dr. Nusser – Dr. Kreisel, the dialysis centers KfH Amberg, KfHBayreuth, KfH Deggendorf, KfH Donauwörth, KfH Freising, KfH Freyung, KfHFürth, KfH Hof, KfH Ingolstadt, KfH Kelheim, KfH München Elsenheimerstraße,KfH München-Schwabing, KfH Neumarkt, KfH Neusäß, KfH Oberschleißheim, KfHPassau, KfH Plauen, KfH Regensburg Günzstraße, KfH Regensburg Caritas-Krankenhaus, KfH Straubing, KfH Sulzbach-Rosenberg, KfH Weiden, Dialysezen-trum Augsburg Dr. Kirschner, Dialysezentrum Bad Alexandersbad, KfH Bamberg,Dialysezentrum Emmering, Dialysezentrum Klinikum Landshut, DialysezentrumLandshut, Dialysezentrum Pfarrkirchen, Dialysezentrum Schwandorf, Dr. AngelaGötz, the medical doctoral student Johanna Christ, and the study nurse IngridLugauer. The expert technical assistance of Claudia Strohmeier is gratefully

acknowledged. Phenotyping was funded by the Dr. Robert Pfleger-Stiftung(C.A.B.), the MSD Stipend Diabetes (C.A.B.), and the University Hospital ofRegensburg (intramural grant ReForM A to Dr. Angela Götz, ReForM C to C.A.B.).Genome-wide genotyping was funded by the KfH Stiftung Präventivmedizin e.V.(C.A.B., Dr. Jens Brüning), the Else Kröner-Fresenius-Stiftung (2012_A147 to C.A.B.and I.M.H.), and the University Hospital Regensburg (C.A.B.). Data analysis wasfunded by the Else Kröner-Fresenius Stiftung (2012_A147 to I.M.H. and C.A.B.and P48/08//A11/08 to C.A.B. and B.K.K.). GENDIAN Study Group: M.G., I.M.H.,B.K.K., M.R., Michael Broll, Alexander Lammert, Jens Brüning, M.O., Klaus Stark,Claudia Strohmeier, Simone Neumeier, Sarah Hufnagel, Petra Jackermeier,Emilia Ruff, Johanna Christ, Peter Nürnberg, Thomas Haak, and C.A.B.

INCIPE Study: The INCIPE (Initiative on Nephropathy, of relevance to publichealth, which is Chronic, possibly in its Initial stages, and carries a Potential riskof major clinical End-points) study was cosponsored by Fondazione Cassa diRisparmio di Verona, Azienda Ospedaliera di Verona, and University of Verona,Italy. N.S.’s research is supported by the Wellcome Trust (grant codes WT098051and WT091310), the European Union Framework Program 7 (EPIGENESYS grantcode 257082 and BLUEPRINT grant code HEALTH-F5-2011-282510).

KORA-F3/KORA-F4 Study: The genetic epidemiological work was funded bythe Munich Center of Health Sciences (MC Health) as part of LMUinnovativ, andby the Else Kröner-Fresenius-Stiftung (P48/08//A11/08 to C.A.B., B.K.K. and2012_A147 to C.A.B. and I.M.H.). The kidney parameter measurements in F3were funded by the Else Kröner-Fresenius-Stiftung (C.A.B., B.K.K.) and theRegensburg University Medical Center, Germany, and in F4 by the University ofUlm, Germany (W.K.). De novo genotyping as well as partly genome-wide geno-typing costs in F3 and F4 were funded by the Else Kröner-Fresenius-Stiftung(C.A.B., B.K.K.). The KORA research platform and the MONICA (MultinationalMONItoring of Trends and Determinants in CArdiovascular Disease) Augsburg stud-ies were initiated and financed by the Helmholtz Zentrum München, German Re-search Center for Environmental Health, by the German Federal Ministry ofEducation and Research and by the State of Bavaria. Genotyping was performedin the Genome Analysis Center (GAC) of the Helmholtz Zentrum München. TheLINUX platform for computation were funded by the University of Regensburg forthe Department of Epidemiology and Preventive Medicine at the RegensburgUniversity Medical Center.

LifeLines Study: The LifeLines Cohort Study and generation and managementof GWAS genotype data for the LifeLines Cohort Study is supported by theNetherlands Organization of Scientific Research NWO grant 175.010.2007.006,the Economic Structure Enhancing Fund (FES) of the Dutch government, theMinistry of Economic Affairs, the Ministry of Education, Culture and Science, theMinistry for Health, Welfare and Sports, the Northern Netherlands Collaboration ofProvinces (SNN), the Province of Groningen, University Medical Center Groningen,the University of Groningen, Dutch Kidney Foundation, and Dutch DiabetesResearch Foundation. LifeLines is a multidisciplinary prospective population-based cohort study examining in a unique three-generation design the health andhealth-related behaviors of 165,000 individuals living in the Northeast region ofthe Netherlands. It uses a broad range of investigative procedures in assessingthe biomedical, sociodemographic, behavioral, physical, and psychologicalfactors that contribute to the health and disease of the general population, witha special focus on multimorbidity and complex genetics.

MESA (The Multi-Ethnic Study of Atherosclerosis) Study: University ofWashington (N01-HC-95159), Regents of the University of California (N01-HC-95160), Columbia University (N01-HC-95161), Johns Hopkins University(N01-HC-95162, N01-HC-95168), University of Minnesota (N01-HC-95163),Northwestern University (N01-HC-95164), Wake Forest University (N01-HC-95165), University of Vermont (N01-HC-95166), New England MedicalCenter (N01-HC-95167), Harbor-UCLA Research and Education Institute(N01-HC-95169), Cedars-Sinai Medical Center (R01-HL-071205), Universityof Virginia (subcontract to R01-HL-071205).

MICROS (Microisolates in South Tyrol) Study: The authors are grateful to allparticipants. The authors thank the primary care practitioners Raffaela Stocker,Stefan Waldner, Toni Pizzecco, Josef Plangger, Ugo Marcadent, and the personnel

diabetes.diabetesjournals.org Teumer and Associates 815

of the Hospital of Silandro Department of Laboratory Medicine for their participationand collaboration in the research project. The authors thank Dr. Peter Riegler(Hemodialysis Unit, Hospital of Merano) for the important discussions. In SouthTyrol, the study was supported by the Ministry of Health and Department ofEducational Assistance, University and Research of the Autonomous Province ofBolzano, the South Tyrolean Sparkasse Foundation, and the European UnionFramework Program 6 EUROSPAN project (contract No. LSHG-CT-2006-018947).

PREVEND Study: PREVEND (Prevention of Renal and Vascular End-stageDisease) genetics is supported by the Dutch Kidney Foundation grant E033; theEU project grant GENECURE (FP-6 LSHM CT 2006 037697); the NIH grant 2R01-LM0-10098; The Netherlands Organisation for Health Research and DevelopmentNWO-Groot grant 175.010.2007.006, NWO VENI grant 916.761.70, ZonMw grant90.700.441; and the Dutch Inter University Cardiology Institute Netherlands(ICIN).

SAPHIR Study: The SAPHIR (Salzburg Atherosclerosis Prevention Program inSubjects at High Individual Risk) study was partially supported by a grant fromthe Kamillo Eisner Stiftung to B.P. and by grants from the Genomics of Lipid-associated Disorders (GOLD) of the “Austrian Genome Research ProgrammeGEN-AU” to F.K.

SHIP/SHIP-Trend/GANI_MED Study: SHIP is part of the Community MedicineResearch net of the University of Greifswald, Germany, which is funded by theFederal Ministry of Education and Research grants 01ZZ9603, 01ZZ0103, and01ZZ0403; the Ministry of Cultural Affairs, as well as the Social Ministry of theFederal State of Mecklenburg-West Pomerania, and the network “GreifswaldApproach to Individualized Medicine (GANI_MED)” funded by the Federal Ministryof Education and Research grant 03IS2061A. Genome-wide data have beensupported by the Federal Ministry of Education and Research grant 03ZIK012and a joint grant from Siemens Healthcare, Erlangen, Germany, and the FederalState of Mecklenburg-West Pomerania. The University of Greifswald is a memberof the “Center of Knowledge Interchange” program of the Siemens AG, theGerman Centre for Diabetes Research (DZD), and the Caché Campus programof the InterSystems GmbH. The SHIP authors are grateful to Mario Stanke for theopportunity to use his Server Cluster for the SNP imputation as well as to HolgerProkisch and Thomas Meitinger (Helmholtz Zentrum München) for the genotypingof the SHIP-Trend cohort.

SKIPOGH (Swiss Kidney Project on Genes in Hypertension) Study: Thisresearch was funded by grant 33CM30-124087 from the Swiss National ScienceFoundation. The study also received support from Lausanne University Hospital,Geneva University Hospital, and Bern University Hospital, Switzerland. M.B.received support from the Swiss School of Public Health Plus (SSPH+).

Vanderbilt Study: Genotyping was supported by National Institute of GeneralMedical Sciences (NIGMS) RC2-GM092318 (Omni1 and Omni5 array). This workwas supported by the Electronic Medical Records and Genomics (eMERGE)Network, initiated and funded by the National Human Genome Research Institute,with additional funding from the NIGMS, through U01-HG04603 (660 array).

Susztak Laboratory: Work in the laboratory of K.S. was supported by NIH R01-DK0-76077 and DK0-87635.

Jacobs Laboratory: Work in the H.J.J. and J.L. laboratories was supported by2R01-069321 to H.J.J.

Devuyst Laboratory: This work was supported by the European Community’sSeventh Framework Programme (FP7/2007–2013) under grant agreement no.305608 (EURenOmics) and the Swiss National Science Foundation project grant310030_146490.

Additional Data Resources: Data on glycemic traits have been contributed byMAGIC (Meta-Analyses of Glucose and Insulin-related traits Consortium)investigators and have been downloaded from www.magicinvestigators.org.Duality of Interest. C.He. received honoraria from Novartis. J.Ch., J.T.,and P.H. received research grants and honoraria from Servier. M.W. hadconsultancies with Amgen and Novartis and received support from Sanofi. K.S.received research support from Boehringer Ingelheim and was on the advisoryboard of Abbvie. No other potential conflicts of interest relevant to this article werereported.

The sponsors had no role in the study design, analyses, drafting of themanuscript, or the decision to publish.Author Contributions. A.L., B.S., B.K.K., B.P., C.He., C.Me., C.G., G.G.,G.E., H.E.W., H.V., J.C.D., L.J.L., M.J.H., M.N., N.J.W., P.P.P., P.S.W., R.R., R.B.,R.J.C., T.B.H., V.G., A.D.P., J.La., J.T., P.H., H.J.J., C.S.F., and A.K. designed thisstudy. A.K.D., A.R.S., B.K., B.Po., B.K.K., B.Pa., B.D.M., C.Ha., C.He., C.Me., C.G.,D.A., D.S., E.B., F.K., G.B.E., G.W., G.N., G.G., G.E., H.E.W., H.G., H.V., H.B., H.K.,I.M.L., I.R., J.L.H., J.S.B., J.H.Z., J.Ch., J.Co., K.B., L.J.L., L.F., L.K., M.Wo.,M.J.H., M.Wa., M.P., M.B., N.J.W., N.E., O.P., P.v.d.H., P.P.P., P.V., P.S.W., R.T.G.,R.R., R.K., S.C., S.B., T.B.H., T.C., T.I., T.T., U.L., U.V., V.G., W.K., I.H.d.B.,J.La., P.H., C.S.F., C.P., and A.K. were involved in the study management. A.R.S.,A.L., B.K.K., B.Pa., C.He., C.Me., C.G., G.G., G.E., H.E.W., H.W., H.V., J.L.H., J.Co.,M.N., L.K., N.J.W., O.P., P.v.d.H., P.P.P., P.S.W., R.J.C., R.T.G., S.S., V.G., O.D.,P.H., and C.S.F. recruited the subjects. A.Te., A.Ti., R.S., M.G., N.C.Y., M.L., Y.L.,V.M., B.O.T., A.V.S., B.Pa., F.K., J.Co., L.K., M.J.H., M.R., S.C., T.A., A.D.P., J.La.,K.E., J.T., P.H., C.S.F., C.P., and A.K. interpreted the results. A.Te., A.Ti., M.G.,N.C.Y., M.L., Y.-A.K., M.J.H., I.M.H., J.La., K.E., K.S., J.T., P.H., H.J.J., C.A.B.,C.P., and A.K. drafted the manuscript. A.Te., A.Ti., R.S., M.G., N.C.Y., A.Y.C.,M.L., Y.L., V.M., Y.-A.K., D.T., M.-H.C., Q.Y., M.C.F., M.O., L.T.H., B.O.T., C.F.,A.V.S., B.K., C.Ha., C.M.S., C.Mü., G.L., J.Lu., M.M., M.J.H., M.R., N.V., R.J.C.,R.K., S.-J.H., S.E.R., T.A., Z.K., J.R.O., A.P., I.M.H., C.A.B., A.D.P., K.E., K.S., C.P.,and A.K. developed statistical methods and performed the analyses. A.R.S.,A.M.Z., B.D.M., C.Ha., C.L., E.B., G.H., H.G., M.H., M.Wa., M.R., N.S., O.P.,R.J.F.L., S.C., T.Z., T.I., and U.V. performed the genotyping. M.G., Y.L., V.M.,Y.-A.K., D.T., C.M.S., C.Mü., G.M., J.C.L., J.Lu., N.V., P.v.d.H., V.C., J.R.O., I.M.H.,K.S., and C.A.B. conducted the bioinformatics analyses. N.C.Y., A.L., A.M.Z.,M.J.H., O.D., J.La., and H.J.J. did the animal work or provided functional data.All authors critically reviewed the manuscript. A.Te., C.S.F., C.P., and A.K. are theguarantors of this work and, as such, had full access to all the data in the studyand take responsibility for the integrity of the data and the accuracy of the dataanalysis.Prior Presentation. Findings from this study were presented at the 51stCongress of the European Renal Association (ERA)-European Diabetes andTransplant Association (EDTA), Amsterdam, the Netherlands, 31 May–3 June2014, and at the Cohorts for Heart and Aging Research in Genomic Epidemiology(CHARGE) Investigator Meeting, Jackson, MS, 1–2 July 2015. An abstract andplatform presentation of this work was presented at the American Society of HumanGenetics (ASHG) 2015 Annual Meeting, Baltimore, MD, 6–10 October 2015.

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